beyond machine learning...circulation what information goes into creating well offset ... bit record...

AI & COGNITIVE SYSTEMS FORUM | 29-31 OCTOBER 2019

BEYOND MACHINE LEARNING

DEVELOPING TRULY INTELLIGENT SYSTEMS BY EMBEDDING HUMAN EXPERTISE TO DIGITAL TWIN

BEYOND MACHINE LEARNINGTERESA TUNG

Copyright © 2017 Accenture. All rights reserved. 3


DIGITAL TWIN

DATA

DIGITIAL TWIN

OLDWORK PROCESS

NEW / MODIFIEDWORK PROCESS

CREATE VALUE

• New markets• Time to market• Regulatory compliance• HSE• Production quality• Yield• OPEX

2D Drawings

Estimates

Photos

VideosProcess knowldege

3D Point Clouds

3D Meshes

Commercial data

Certificates

IoT Sensor data

3D Models

Transactions

Analytics & AI

PLM

Supply Chain

IoT

MESMOM

CAD

DIGITAL TWIN IS A DIGITAL MODEL OF A PHYSICAL OBJECT, SYSTEM OR PROCESS THAT EXISTS (OR COULD EXIST)



EXAMPLE: DIGITAL TWIN FOR OIL WELL

DIGITIAL TWIN

Well DataField dataEWRDDRAFECosts

ProductionDecline CurvesHistorianReal-Time OperationsComplianceERP

ML supports Continuous Improvement Across

Multidisciplinary teams

ML generates Optimal Well Design and Execution plan

AI-driven Drilling & Completion

Predictive Analytics on Integrated Digital Supply Chain

Real-Time Analytics for Wells Optimization Center

WELL DESIGN DRILLING & COMPLETION

Analytics & AI

PLM

Supply Chain

IoT

MESMOM

CAD



+HUMAN EXPERTISE

LIFECYCLE OF THING / SYSTEM

DTLeverage past performance results to generate designrequirements DT

Reconcile as-designed and as-built through manufacturing build and commisioning

DT

Leverage past designs to generate engineering detailed design to fulfill requirements

DTApply cross system lessons with instance-specific needs to optimize operations

ADDS THE CONTEXT FOR DIFFERENT VIEWS OF THE DIGITAL TWIN

What results are interesting? E.g., high productivity, loss circulation

What information goes into creating well offset study?

What measurements are used? What is acceptable variance?

What is the insight from the specific pattern of events? What is the next-best action?



EXAMPLE: +HUMAN EXPERTISE IN SEARCHEXAMPLE: INTERNET SEARCH FOR ACCENTURE



KNOWLEDGE GRAPH

Extract Information Contextualize Information Assemble Information

Painting

Is a

Mona Lisala Joconde a’Washington

Person

talks about was created by

Is a

Leonardo Da’Vinci

was created by

EXAMPLE: INTERNET SEARCH



DIGITAL TWIN + HUMAN EXPERIENCE

CREATE A DESIGN IDENTIFY A ROOT CAUSE REDEFINE A PROCESS

Fall, Slip Notice, Walk

Fall, Trip,

Puncture

Floor,

Slip, Leak

Water,

Oil,

Leak

Floor,

Water,

Slip

Pole, Wire,

Crew

Work,

Area,

Hazard

Electric,

Pole,

Crew

Wasp, Snakes

“Safety event”



MODEL HUMAN REASONING

Start with the questions

• How should we plan our next well?

• What are similar offset wells?

• What are events I should be aware of?

• What sort of impact do they have?

• How often does it happen?

• Under what conditions?

Example Model Schema



POPULATE THE GRAPH

Non-Exhaustive

Data Sources

DDRBit recordNOOP &

AFE

FWR FDPHIS

Report

Post Drilling

Report

Final

Geologic

al Report

Final

Cement

Report

Mud LogsRushmore

Final

DD/MWD

Report

Structured Unstructured Legend:

Drilling Data TypeDrilling Report

Drilling Databases

• Well Architecture (stick chart)

• Actual Mud weight ranges, cement

type & quality

• Downhole related problems

• NPT data

• Lessons Learnt/After Action

Review/TL Performance

• Well Depth

• Well Cost & Days

• Rig type

• Time and date of historical well

• Geological & Lithological Prognosis

• Any relevant regional info/issues

• Sidetracking method & info

…

Example from Drilling Report:

Skid out cantilever 50 ft to

transom aftwards, 2 ft transverse

portwards to well center and

secure same.

300m

Example Well Design Schematic

200m

Natural Language

Processing• Regular Expressions

• Dictionary-based approaches

• Pattern-based approaches

• Machine Learning

• Other combinations

Image Processing• Image Recognition

• Image Detection

• Image Classification

• Edge Detection

• Corner Detection

Relational Data Processing• Schema Parsing

• Entity Relationship Diagram to Hierarchy

Parsing

• Data Normalization/ Denormalization

Extract from Original System



WELL ADVISOR: OFFSET STUDY

1

Identify population of wells

that meet the search criteria




Copyright © 2019 Accenture. All rights reserved.

2

Generate a

unique profile

based on the

population

3

Recommend

similar wells

to the profile or

identify outliers

Measurements

Well Parts Materials

Rig PartsOperations




4

Identify

commonalities

or abnormalities

in the curated

population

5

Track remediation and explain

consequences or commonalities.

Event

Type

Operation

Type 1

Operation

Type 2

Event

Type

Corrective

Action 1

Operation

Type 2

Corrective

Action 2Remediation



YOU NEED HUMAN EXPERIENCE TO…

+HUMAN EXPERIENCE

• Configure and Apply Digital Twin

• Handle Domain-specific models, processes, regulations

• Augment Gaps in Data

Populate with Data

Exploit Knowledge

Capture & Curate Human Reasoning

AI & COGNITIVE SYSTEMS FORUM | 29-31 OCTOBER 2019

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beyond machine learning...circulation what information goes into creating well offset ... bit record...

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